Unified Resource Allocation and Mobility Management Technique Using Particle Swarm Optimization for VLC Networks

Demir, M. Selim; Sait, Sadiq M.; Uysal, Murat

doi:10.1109/jphot.2018.2864139

Cited by 21 publications

(27 citation statements)

References 27 publications

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“…The second is the IG heuristic [13], which is a competitor for SA because of its quick convergence and simple design that closely matches with that of the SA. The third is PSO for the RA problem as proposed by Demir et al [16]. The fourth is the standard TS algorithm [13].…”

Section: Simulation Resultsmentioning

confidence: 99%

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Resource Allocation for Visible Light Communication Systems Using Simulated Annealing Based on a Problem-Specific Neighbor Function

et al. 2019

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In this paper, we consider a visible light communication (VLC) system with direct currentbiased orthogonal frequency division multiplexing (DC-OFDM) and investigate resource allocation for a multi-user environment. Based on the user satisfaction index as a function of data rate, we aim to optimally determine the allocation of the users to different LEDs (acting as access points) and OFDM subcarriers. We propose a simulated annealing-based heuristic to maximize the average user satisfaction index. In an effort to make the proposed solution practically feasible, the runtime of the proposed heuristic is kept less than the channel coherence time, whose value is in order of tens of milliseconds. We evaluate the performance of the proposed heuristic algorithm in different scenarios that vary in the number of users, the number of LEDs, and the separation between users. Our results demonstrate that the proposed heuristic outperforms other wellknown heuristics (such as standard simulated annealing, iterative greedy, particle swarm optimization, and tabu search) while achieving good quality solutions within a short execution time, i.e., 40-80 ms.INDEX TERMS Visible light communications, resource allocation, optimization, simulated annealing, heuristics.

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Section: Simulation Resultsmentioning

confidence: 99%

“…They formulated the maximization as a multi-choice knapsack problem and solved it using a heuristic based on dynamic programming. Recently, Demir et al [15], [16] applied the GA and Particle Swarm Optimization (PSO) metaheuristic algorithms for resource allocation in DCO-OFDM systems to maximize the data rate and user fairness index.…”

Section: Introductionmentioning

confidence: 99%

Resource Allocation for Visible Light Communication Systems Using Simulated Annealing Based on a Problem-Specific Neighbor Function

et al. 2019

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“…In [22], the authors formulated an optimization problem in terms of power and bandwidth allocation in order to maximize the energy efficiency of a heterogeneous VLC RF communication system. In [23], the authors have presented unified resource allocation and mobility management algorithm for indoor VLC networks based upon particle swarm optimization. It is important to note that all these schemes are formulated as optimization problems to be solved at every time-step, therefore, resulting into a high computational complexity.…”

Section: A Related Workmentioning

confidence: 99%

Reinforcement Learning Based Load Balancing for Hybrid LiFi WiFi Networks

et al. 2020

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Light fidelity (LiFi) is an emerging communication technology, which utilizes the lightemitting diodes (LEDs) for high-speed wireless communications. Due to its huge unlicensed bandwidth, LiFi is capable of supporting high data rates. The quality of the LiFi channel fluctuates across the room due to interference, reflection from walls or blockage. On the other hand, WiFi is another wireless communication technology that is capable of providing moderate data rates with ubiquitous coverage. As the electromagnetic spectrum of LiFi does not overlap with WiFi, both of them can coexist to form a hybrid LiFi and WiFi network for seamless and high-throughput connectivity. The performance of a hybrid system significantly depends upon the access point (AP) assignment and resource allocation strategies. In this paper, a downlink hybrid system with one WiFi AP and four LiFi APs is considered, and a reinforcement learning (RL) algorithm is implemented in order to determine an optimal AP assignment strategy, which maximizes the long-term system throughput while ensuring the required users fairness and satisfaction. Furthermore, two different scenarios based on the random waypoint model with uniform and non-uniform distribution of users have been studied. The performance of the proposed system is compared against state-of-the-art benchmark approaches e.g., signal strength strategy (SSS), exhaustive search, and an iterative optimization method. The results are reported in terms of the average system throughput, user satisfaction, fairness, and capacity outage probability. It is shown that the proposed RL method performs closer to the exhaustive search scheme at fairly low complexity. The RL method also outperforms the SSS scheme and the iterative algorithm in most scenarios INDEX TERMS Hybrid LiFi WiFi, Light Fidelity (LiFi), Load balancing, Reinforcement learning (RL), Trust region policy optimization (TRPO).

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“…It is noted that this is a common assumption in related literature validated by the fact that light signals do not penetrate through walls, which enables having an interference-free LiFi attocell built into a room [32], [36]. Furthermore, the AP is assumed to have channel state information (CSI) of all users in its coverage area, which is a common assumption in related literature [27], [37], [38].…”

Section: Lifi Downlink Frameworkmentioning

confidence: 99%

Dynamic Multiple Access Configuration in Intelligent Lifi Attocellular Access Points

2019

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The exponential growth in the global demand for wireless connectivity calls for efficient and reliable management of the available wireless resources. Light fidelity (LiFi) harnesses the vast untapped wireless transmission resources in the infrared spectrum and visible light spectrum to create ultra-dense wireless networks which support user mobility, multiuser access, and handover. Various multiuser access (MA) protocols have been developed to meet the varying system requirements, including orthogonal multiple access (OMA) and non-orthogonal multiple access (NOMA) schemes. While NOMA, on the one hand, allows for significant enhancement in the achievable data rates, its performance may be severely degraded under particular conditions such as a large number of connected users or users existing in highly symmetrical locations. OMA, on the other hand, provides better link reliability in such scenarios but at the expense of decreased spectral efficiency. Therefore, there is a need to enable a degree of intelligence in the LiFi access point (AP) to facilitate real-time configuration of the MA protocol. To this end, this paper develops a novel cross-layer design framework for dynamic multiple access selections (DMAS) in intelligent LiFi APs. The developed framework runs at LiFi attocell system level and can be configured to cater for various system requirements in terms of sum data rate, average outage probability, and fairness. The obtained results show that DMAS introduces an effective solution for multiuser resource allocation by achieving better satisfaction of the system requirements compared to the static configuration of a single MA scheme.INDEX TERMS Light fidelity, visible light communications, multiuser access, OMA, NOMA, MAC protocol, LiFi intelligence.

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Unified Resource Allocation and Mobility Management Technique Using Particle Swarm Optimization for VLC Networks

Cited by 21 publications

References 27 publications

Resource Allocation for Visible Light Communication Systems Using Simulated Annealing Based on a Problem-Specific Neighbor Function

Resource Allocation for Visible Light Communication Systems Using Simulated Annealing Based on a Problem-Specific Neighbor Function

Reinforcement Learning Based Load Balancing for Hybrid LiFi WiFi Networks

Dynamic Multiple Access Configuration in Intelligent Lifi Attocellular Access Points

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